Combining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. This intermediate level class bridges between Data8 and upper division computer science and statistics courses as well as methods courses in other fields.

The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

Launching today, the 2019 edition of Practical Deep Learning for Coders, the third iteration of the course, is 100% new material, including applications that have never been covered by an introductory deep learning course before (with some techniques that haven’t even been published in academic papers yet). There are seven lessons, each around 2 hours long, and you should plan to spend about 10 hours on assignments for each lesson.

This is the Curriculum for Learn Data Science in 3 Months by Siraj Raval on Youtube. After completing this course, start applying for jobs, doing contract work, start your own data science consulting group, or just keep on learning.

The rule of three gives a quick and dirty way to estimate these kinds of probabilities. It says that if you’ve tested N cases and haven’t found what you’re looking for, a reasonable estimate is that the probability is less than 3/N.

In this article, I am going to list 6 books that I recommend to start with to learn statistics. The first three are lighter reads. These books are really good for setting your mind to think more numerical, mathematical and statistical. They also present why statistics is exciting (it is!) really well.